Steam trap monitoring devices, systems, and related techniques

ABSTRACT

Devices, systems, and techniques relating to steam trap monitoring are described. These include battery-less steam trap monitors that run on power harvested from their environments, systems for acquiring steam trap monitor data for the traps in a facility or across multiple facilities, and techniques for processing steam trap monitor data to reliably determine the status of individual steam traps and potentially other system parameters.

INCORPORATION BY REFERENCE

An Application Data Sheet is filed concurrently with this specificationas part of this application. Each application to which this applicationclaims benefit or priority as identified in the concurrently filedApplication Data Sheet is incorporated by reference herein in itsentirety and for all purposes.

BACKGROUND

Steam systems provide energy via heat transfer in a wide variety ofindustrial applications. Steam generated by a boiler flows through adistribution system to heat exchangers by which the heat of the steam istransferred to loads. As heat is transferred, either to the loads towhich the energy is directed or as losses from the distribution system,at least some of the steam condenses to liquid form (condensate). Thiscondensate reduces the efficiency of the heat transfer of the system.Condensate can also result in catastrophic damage to the system ifaccelerated to high speeds by the remaining live steam. Non-condensablegases (e.g., oxygen and carbon dioxide) that enter or form in the systemalso reduce system efficiency and can contribute to corrosion of systemcomponents.

Steam traps are devices designed to discharge condensates andnon-condensable gases from steam systems, preferably with minimal lossof steam. There are currently several basic types of steam trapsincluding, for example, mechanical traps, temperature traps,thermodynamic traps, Venturi nozzle traps, etc. Each type has advantagesand disadvantages that affect its desirability for a given application.And while steam traps are effective at maintaining the efficiency ofsteam systems, they are characterized by failure modes that can resultin system inefficiency and down time and so must be regularly inspectedand maintained. But manual inspection of steam traps can be expensive,or even impractical, particularly for facilities in which there might behundreds or even thousands of steam traps.

To address this issue, electronic steam trap monitors have beendeveloped that automatically monitor parameters of a steam trap withwhich they are associated and transmit fault signals when thoseparameters cross acceptable operational thresholds. However, most steamtrap monitors on the market today have issues both with the reliabilitywith which they detect fault conditions, and the fact that most arebattery powered and therefore require periodic battery inspection and/orreplacement, therefore at least partially defeating the purpose forwhich they were installed.

SUMMARY

According to various implementations, methods, apparatus, devices,systems, and computer program products are provided at least some ofwhich relate to steam trap monitoring.

According to a particular class of implementations, methods, apparatus,devices, systems, and computer program products are provided in whichsteam trap data for a steam trap are received. The steam trap datainclude time series data representing one or both of steam temperatureand condensate temperature. Normal baseline operation of the steam trapis determined based on a first range of the time series data. A changein operation of the steam trap is determined based on one or more datapoints of the time series data.

According to a specific implementation of this class, the time seriesdata include first time series data representing the steam temperatureand second time series data representing the condensate temperature.Determining the normal baseline operation of the steam trap includes:generating a plurality of vectors using the steam trap data, theplurality of vectors including one or more steam temperature envelopevectors based on the first time series data, and one or more condensatetemperature envelope vectors based on the second time series data;determining one or more periods during which live steam is flowing atthe steam trap based on the first time series data; and based ondetermining the one or more periods and the normal baseline operation ofthe steam trap, processing a first subset of the vectors to determine astate of the steam trap during at least one of the periods.

According to a more specific implementation, the state of the steam trapincludes one of normal, failed open, modulating, or failed closed.

According to another more specific implementation, determining the oneor more periods includes: using a plurality of methods to determine aplurality of on states and a plurality of off states based on the firsttime series data using each of the methods, each of the on states andeach off states having a confidence value associated therewith; andusing the confidence values, combining or selecting from among the onand off states for the plurality of methods to define the one or moreperiods.

According to another more specific implementation, determining the oneor more periods includes determining a set of floating thresholds basedon the one or more steam temperature envelope vectors, and determiningpoints at which the first time series data are above or belowcorresponding floating thresholds.

According to another more specific implementation, determining the oneor more periods includes determining points at which the first timeseries data are above or below a fixed threshold using a range ofhysteresis around the fixed threshold.

According to another more specific implementation, determining the oneor more periods includes determining a first point at which the firsttime series data is below a threshold, and identifying a second point inthe first time series data earlier than the first point as correspondingto a steam off event. According to an even more specific implementation,the second point is identified using a sample time interval vector basedon either or both of the first and second time series data.

According to another more specific implementation, determining the oneor more periods includes determining a threshold based on a ground truthevent representing a baseline for normal operation of the steam trap,and determining points at which the first time series data are above orbelow the threshold.

According to specific implementation of this class, determining thenormal baseline operation of the steam trap is based, at least in part,on determining, based on the time series data, that the steam trap is ina modulated steam application.

According to a more specific implementation, determining that the steamtrap is in a modulated steam application includes applying a fastFourier transform to at least a portion of the time series data, orapplying an autocorrelation function to at least a portion of the timeseries data.

According to a specific implementation of this class, the time seriesdata include first time series data representing the steam temperatureand second time series data representing the condensate temperature, anddetermining the normal baseline operation of the steam trap includes:generating a plurality of vectors using the steam trap data, theplurality of vectors including one or more steam temperature envelopevectors based on the first time series data, and one or more condensatetemperature envelope vectors based on the second time series data; anddetermining a centroid of a set of data points using the one or moresteam temperature envelope vectors or the one or more condensatetemperature envelope vectors, each data point representing correspondingvalues of the first or second time series data. Determining a change inoperation of the steam trap includes: tracking deviations of the datapoints relative to the centroid of the data points; and identifying asubset of the deviations having corresponding magnitudes larger than athreshold and corresponding angles within a specified angle range.

According to a more specific implementation, the threshold is based on avariance of the data points. According to an even more specificimplementation, the threshold is defined based on a specific number ofstandard deviations of the variance.

According to another more specific implementation, steam trap metadataare received. The steam trap metadata represent a steam trap type of thesteam trap and a steam trap application of the steam trap. The thresholdand specified angle range are based on the steam trap type and the steamtrap application.

According to a specific implementation of this class, steam trapmetadata are received. The steam trap metadata represent a steam traptype of the steam trap and a steam trap application of the steam trap.Determining the normal baseline operation of the steam trap is based onthe steam trap type and the steam trap application.

According to another class of implementations, a sensing device includesone or more sensors configured to generate sensor signals, one or moretransmitters configured to transmit sensor data representing the sensorsignals, and control circuitry configured to control operation of theone or more transmitters, and to convert the sensor signals to thesensor data. A first energy harvesting device is configured to harvest afirst type of energy from an environment in which the sensing device isdeployed. A second energy harvesting device is configured to harvest asecond type of energy from the environment, the second type of energybeing different from the first type of energy. An energy storage deviceis configured to store energy derived from the first and second types ofenergy. Power management circuitry is configured to receive first powerfrom the first energy harvesting device and second power from the secondenergy harvesting device, and control storing of the stored energy bythe energy storage device. The power management circuitry is furtherconfigured to provide third power generated from the stored energy tothe one or more transmitters and the control circuitry. The powermanagement circuitry is also configured to operate in a first mode inwhich the stored energy is derived only from the first power, in asecond mode in which the stored energy is derived only from the secondpower, and in a third mode in which the stored energy is derived from acombination of the first power and the second power.

According to a specific implementation of this class, each of the firstand second types of energy is one of light energy, mechanical energy,heat energy, vibrational energy, acoustic energy, ultrasonic energy,radio frequency energy, electromagnetic energy, or magnetic energy.

According to a specific implementation of this class, the powermanagement circuitry includes at least one of a DC-DC converter, anAC-DC converter, a buck converter, a boost converter, a buck-boostconverter, or a single inductor multiple output (SIMO) converter.

According to a specific implementation of this class, the energy storagedevice is one of a capacitor, or a battery.

According to a specific implementation of this class, the sensing deviceis configured to monitor a steam trap. The one or more sensors include afirst temperature sensor and a second temperature sensor, and the sensordata represent steam temperature and condensate temperature.

According to a more specific implementation, the sensor data includefirst time series data representing the steam temperature and secondtime series data representing the condensate temperature.

According to another more specific implementation, the first energyharvesting device comprises a thermo-electric generator and the secondenergy harvesting device comprises one or more photovoltaic cells.

According to a specific implementation of this class, selectioncircuitry is configured to selectively enable the power managementcircuitry to operate in the first, second, or third modes.

According to a specific implementation of this class, the powermanagement circuitry includes power-point-tracking circuitry configuredto track either or both of the first power generated by the first energyharvesting device and the second power generated by the second energyharvesting device.

According to another class of implementations, a sensing device includesone or more temperature sensors configured to generate sensor signalsrepresenting one or more temperatures, one or more transmittersconfigured to wirelessly transmit sensor data representing the sensorsignals to a remote device, and control circuitry configured to controloperation of the one or more transmitters, and to convert the sensorsignals to the sensor data. The sensor data include time series datarepresenting the one or more temperatures. A thermo-electric generatoris configured to harvest heat energy from an environment in which thesensing device is deployed. An energy storage device is configured tostore energy derived from the heat energy.

According to a specific implementation of this class, a second energyharvesting device is configured to harvest a second type of energy fromthe environment, the second type of energy being different from heatenergy. Power management circuitry is configured to receive first powerfrom the thermo-electric generator and second power from the secondenergy harvesting device, and control storing of the stored energy bythe energy storage device. The power management circuitry is furtherconfigured to provide third power generated from the stored energy tothe one or more transmitters and the control circuitry.

According to a more specific implementation, the power managementcircuitry is further configured to operate in a first mode in which thestored energy is derived only from the first power, in a second mode inwhich the stored energy is derived only from the second power, and in athird mode in which the stored energy is derived from a combination ofthe first power and the second power. According to an even more specificimplementation, selection circuitry is configured to selectively enablethe power management circuitry to operate in the first, second, or thirdmodes.

According to another more specific implementation, the power managementcircuitry includes power-point-tracking circuitry configured to trackeither or both of the first power generated by the thermo-electricgenerator and the second power generated by the second energy harvestingdevice.

According to another more specific implementation, the second type ofenergy is one of light energy, mechanical energy, vibrational energy,acoustic energy, ultrasonic energy, radio frequency energy,electromagnetic energy, or magnetic energy.

According to another more specific implementation, the power managementcircuitry includes at least one of a DC-DC converter, an AC-DCconverter, a buck converter, a boost converter, a buck-boost converter,or a single inductor multiple output (SIMO) converter.

According to another more specific implementation, the second energyharvesting device comprises one or more photovoltaic cells.

According to a specific implementation of this class, the energy storagedevice is one of a capacitor, or a battery.

According to a specific implementation of this class, the sensing deviceis configured to monitor a steam trap, wherein the one or moretemperature sensors include a first temperature sensor and a secondtemperature sensor, and wherein the sensor data represent steamtemperature and condensate temperature.

According to a specific implementation of this class, the sensor datainclude first time series data representing the steam temperature andsecond time series data representing the condensate temperature.

A further understanding of the nature and advantages of variousimplementations may be realized by reference to the remaining portionsof the specification and the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example of a steam system and a cloud-connected steamtrap monitoring system enabled by the present disclosure.

FIG. 2 is a block diagram of steam trap monitor enabled by the presentdisclosure.

FIG. 3 is a flow diagram illustrating processing of steam trap monitordata as enabled by the present disclosure.

FIG. 4 is another flow diagram illustrating processing of steam trapmonitor data as enabled by the present disclosure.

FIG. 5 is another flow diagram illustrating processing of steam trapmonitor data as enabled by the present disclosure.

FIG. 6 is another flow diagram illustrating processing of steam trapmonitor data as enabled by the present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to specific implementations.Examples of these implementations are illustrated in the accompanyingdrawings. It should be noted that these examples are described forillustrative purposes and are not intended to limit the scope of thisdisclosure. Rather, alternatives, modifications, and equivalents of thedescribed implementations are included within the scope of thisdisclosure as defined by the appended claims. In addition, specificdetails may be provided in order to promote a thorough understanding ofthe described implementations. Some implementations within the scope ofthis disclosure may be practiced without some or all of these details.Further, well known features may not have been described in detail forthe sake of clarity.

The present disclosure describes various devices, systems, andtechniques relating to steam trap monitoring, including battery-lesssteam trap monitors that run on power harvested from their environments,systems for acquiring steam trap monitor data for the traps in afacility or across multiple facilities, and techniques for processingsteam trap monitor data to reliably determine the status of individualsteam traps and potentially other system parameters. It should be notedthat the described examples may be used in various combinations. Itshould also be noted that at least some of the examples described hereinmay be implemented independently of the others. For example, thetechniques described herein for processing steam trap monitor data maybe employed to process data captured using any of a wide variety ofmonitors including, but not limited to, the monitors described herein.Similarly, the steam trap monitors described herein may be used with anyof a wide variety of monitoring systems and data processing techniquesincluding, but not limited to, the systems and techniques describedherein.

FIG. 1 depicts a steam trap monitoring system 100 in which multiplesteam traps 102 (potentially hundreds or even thousands) are deployedthroughout a facility that employs a steam system. The details of thesteam system are not shown for reasons of clarity. In addition, thesteam traps in FIG. 1 are depicted as conventional inverted-bucket-typesteam traps. However, it should be noted that this is merely one exampleof the types of steam traps that may be monitored as described herein.That is, the systems, monitors, and techniques described herein may beused with any of the four basic types of steam traps, e.g., mechanicaltraps, temperature traps, thermodynamic traps, and Venturi nozzle traps.

Each steam trap 102 has an associated steam trap monitor (STM) 104mounted on or near the steam trap. STMs 104 generate various types ofsensor data relating to the associated steam trap 102 and its adjacentpiping. STMs 104 transmit the sensor data to control nodes 106 that, inturn, transmit the sensor data to an STM data service 108 via network110. As will be appreciated, the number of STMs 104 and control nodes106 will vary depending on the facility.

STM service 108 may conform to any of a wide variety of architecturessuch as, for example, a services platform deployed at one or moreco-locations, each implemented with one or more servers 112. STM service108 may also be partially or entirely implemented using cloud-basedcomputing resources. Network 110 represents any subset or combination ofa wide variety of network environments including, for example, TCP/UDPover IP-based networks, unicast/multicast/broadcast networks,telecommunications networks, wireless networks, satellite networks,cable networks, public networks, private networks, wide area networks,local area networks, the Internet, the World Wide Web, intranets,extranets, and so on.

At least some of the examples described herein contemplateimplementations based on computing models that enable ubiquitous,convenient, on-demand network access to a pool of computing resources(e.g., cloud-based networks, servers, storage, applications, andservices). As will be understood, such computing resources may beintegrated with and/or under the control of the same entity controllingSTM data service 108. Alternatively, such resources may be independentof service 108, e.g., on a platform under control of a separate providerof computing resources with which service 108 connects to consumecomputing resources as needed, e.g., a cloud-computing platform orservice.

It should also be noted that, despite any references to particularcomputing paradigms and software tools herein, the computer programinstructions on which various implementations are based may correspondto any of a wide variety of programming languages, software tools anddata formats, may be stored in any type of non-transitorycomputer-readable storage media or memory device(s), and may be executedaccording to a variety of computing models including, for example, aclient/server model, a peer-to-peer model, on a stand-alone computingdevice, or according to a distributed computing model in which variousfunctionalities may be effected or employed at different locations.

STMs 104 may communicate with control nodes 106 using any of a widevariety of wired and wireless protocols and technologies. According tosome implementations, control nodes 106 and STMs 104 communicate using aproprietary low-power communication protocol known as Evernet™ providedby Everactive, Inc., of Santa Clara, Calif. Examples of such protocolsand associated circuitry suitable for use with such implementations aredescribed in U.S. Pat. Nos. 9,020,456 and 9,413,403, and U.S. PatentPublications No. 2014/0269563 and No. 2016/0037486, the entiredisclosure of each of which is incorporated herein by reference for allpurposes. However, it should be noted that implementations arecontemplated in which other modes of communication between the STMs andthe rest of the system are employed.

Control nodes 106 may be implemented using any of a variety of suitableindustrial Internet gateways, and may connect to STM service 108 usingany of a variety of wired and wireless protocols, e.g., various versionsof Ethernet, various cellular (e.g., 3G, LTE, 5G, etc.), various wi-fi(802.11b/g/n, etc.), etc. In some cases, otherwise conventional gatewaysare augmented to include components that implement the Evernet™protocol.

Each STM 104 generates sensor data representing one or more temperaturesassociated with the steam trap with which it is associated, and possiblyother sensed data associated with the trap. The one or more temperaturesinclude steam temperature and/or condensate temperature. Steamtemperature is captured using a temperature sensor (e.g., a thermistor)connected to the piping of the system side of the trap (which might belive steam or a load). Condensate temperature is captured using atemperature sensor (e.g., a thermistor) connected to the piping throughwhich condensate and non-condensable gases are expelled. The STMs mayalso be configured to capture and generate sensor data representingambient temperature and/or humidity of the environment in which the STMis deployed.

Each STM 104 may also be configured to generate sensor data representinga variety of other parameters generated by a variety of sensor typesand/or sources. For example, an STM might monitor light levels,humidity, vibrational or other types of mechanical energy, acousticenergy, ultrasonic energy, etc.

According to a particular implementation, in response to a wakeupmessage from its control node 106 or a local wakeup timer, each STM 104transitions from a low-power mode, takes readings on each of itssensors, and transmits digitized versions of the readings to its controlnode 106 in a packet in which each sensor and its reading are paired(e.g., as a label-value pair). The packet also includes information(e.g., in a header) that identifies the specific STM with a uniqueidentifier and the timestamp of the readings in the packet. The wakeupmessages may be periodically transmitted from each control node to itsassociated STMs.

Each control node 106 stores the packets received from its STMs 104 inits local database, and periodically or opportunistically uploads thestored information to STM data service 108 (e.g., to a cloud-basedservice when the control node is connected to the Internet). Thus, ifthere is an outage, the control node is able to cache the sensor datauntil the connection is restored. The processing of the sensor data isdone by STM data service 108, e.g., using logic 114. STM data service108 also stores historical data for steam trap monitoring system 100(e.g., in data store 116). Steam trap data and other system datagenerated by STM data service 108 and stored in data store 116 may beaccessed on demand (e.g., in a dashboard on computing device 118) byresponsible personnel associated with the facility or facilities inwhich the steam trap monitoring system is deployed.

Steam temperature and condensate temperature are useful for determiningthe state of a steam trap because a steam trap is designed to collectcondensate that forms in the steam system at or near where the trap isdeployed. A steam trap is usually installed at low points in the steamdistribution system. Every so often, the steam trap expels collectedcondensate into a drain line. When a steam trap fails, it often fails toa condition which results in steam going straight through the trap intothe drain line.

In a simple example, the steam side of a steam trap is typicallyexpected to be at or near the temperature of live steam (e.g., well over100 degrees C.). By contrast, because of the presence of condensate, thecondensate side of the trap is typically expected to be at a lowertemperature than the steam side of the trap. If a steam trap fails open,this may be detected based on a higher than expected temperature at thecondensate side of the trap relative to the steam temperature. However,because of the different types of steam traps, the differentapplications in which they are installed, the diversity of failuremodes, and the inherent noisiness of the temperature data, determiningthe state of the trap using a simple comparison of the two temperaturesmay not be particularly reliable.

As will be discussed below, the present disclosure enables a variety oftechniques by which these and potentially other parameters may beprocessed to determine more reliably the state of an individual steamtrap. In addition, a variety of system parameters beyond the state ofthe individual steam trap may be detected or determined by monitoringthese temperatures and/or other parameters. For example, a dip in steamtemperature might be caused by a drop in pressure at the boiler or apressure reducing valve (PRV). Such a determination might be made basedon a set of data points from one STM and/or data sets from multiple STMsdistributed around the system. Data from multiple STMs may also be usedto make finer-grained assessments such as, for example, distinguishingbetween a pressure problem with the boiler or the pressure reducingvalve (PRV).

More generally, implementations are contemplated in which varioussubsets of STM data (both captured data and derived data) may be used todefine a normal baseline operation (and potentially some range aroundnormal) for any of a wide variety of system behaviors or components.Such definitions of normal may then be used to detect deviations fromthe expected range. This might initially involve the identification of ageneral fault condition, but also could be refined over time to identifyspecific states and/or failure modes as represented by correspondingdata signatures. Such signatures might be represented using datagenerated by one or multiple STMs, at a given point in time, or over aparticular time range.

According to some implementations, STMs are employed that operate usingpower harvested from the environments in which they are deployed. FIG. 2is a block diagram of an example of such an STM 200. In the depictedimplementation, STM 200 is powered using energy harvested from itsenvironment with a photovoltaic (PV) device 202 that captures energyfrom the ambient light in the vicinity of STM 200, and a thermoelectricgenerator (TEG) 204 that captures thermal energy from, for example, thepipes of the steam distribution system. As will be discussed,implementations are contemplated in which the STM's power managementunit may be configured such that the STM can use power from the PVdevice in a “solar only” mode (as indicated by the dashed line from PVdevice 202 to VIN), the TEG in a “TEG only” mode, or a combination ofboth in a “solar assist” mode (as indicated by the solid line from PVdevice 202 to VCAP). Suitable switching circuitry for configuring theseconnections will be known to those of skill in the art and so is notdepicted for clarity.

STM 200 includes a power management unit (PMU) 206 that controls thedelivery of power to controller 208 and data transmitter 210 via loadswitch 212. VIN is the harvesting input to PMU 206, and VCAP, and threevoltage rails (not shown for clarity) are the generated outputs. PMU 206charges energy storage device 214 (e.g., a super-capacitor) with VCAPvia charging circuit 216 using energy harvested from either or both ofPV device 202 and TEG 204 (depending on the harvesting mode). Loadswitch 212 and charging circuit 216 control when power is provided tothe rest of STM 200 and allow STM 200 to be functional while energystorage device 214 is charging.

STM 200 receives a wakeup message (e.g., with wakeup receiver 218) froma system control node with which it is associated. Receipt of the wakeupmessage triggers control of load switch 212 by PMU 206 to provide powerto controller 208 for capturing readings associated with the steam trapbeing monitored by STM 200, and to transmitter 210 for transmittingsensor data to the control node. PMU 206 also communicates withcontroller 208 via digital I/O channel 220. This can be used by thecontroller to monitor the status of the PMU 206, and to update itsconfiguration or calibration settings.

Once awakened and powered up, controller 208 captures readings using oneor more sets of sensors associated with STM 200. As depicted, thesemight include one or more temperature sensors 222 (e.g., thermistorsconnected to the piping adjacent the trap). Sensors to detect or measureother parameters or types of readings (e.g., ambient temperature and/orlight, acoustic, ultrasonic, humidity, vibrational/mechanical energy,etc.) are also contemplated. As discussed above, controller 208packetizes the digitized sensor data and transmits the packet(s) to theassociated sensor node via data transmitter 210.

According to a particular implementation, PMU 206 includes a boost DC-DCconverter that employs maximum power point tracking to boost therelatively low voltage VIN received from one of the harvesting sources(e.g., PV device 202 or TEG 204 depending on the mode) to a highervoltage VCAP at its output that is used to charge the energy storagedevice (e.g., 214). Once VCAP is sufficiently high, a buck/boost, asingle-input-multiple-output (SIMO) DC-DC converter turns on and takesVCAP and brings it up or down (depending on the level of charge ofenergy storage device 214), generating three voltage rails; +2.5, +1.2,and +0.6 volts respectively. These voltage rails are for use in poweringthe other electronics of STM 200 (e.g., controller 208 and transmitter210).

In the “solar assist” harvesting mode, PV device 202 may be attacheddirectly to VCAP through diode 224 (to prevent leakage) as representedby the solid line connection in FIG. 2. In this mode, and assuming itsoutput is sufficient to forward bias diode 224, PV device 202 mayprovide a charging assist to TEG 204 with the energy of the twoharvesting sources naturally combining in energy storage device 214without requiring complicated control electronics. According to aparticular implementation, in the “solar assist” mode, PV device 202 isused to raise VCAP such that the biasing to the boost converter turnson. This allows the boost to harvest from lower input voltages (e.g.,allowing harvesting from lower temperature deltas on TEG 204). Inanother implementation, PV device 202 may connect to VIN of PMU 206 asshown by the dashed line in FIG. 2. This allows for lower levels oflight, or lower voltage PV cells to be boosted to recharge the energystorage element.

More generally, implementations are enabled by the present disclosure inwhich energy may be harvested from multiple different energy sources andused in any combination to power such an STM. Other potential sourcesfor harvesting include vibration energy (e.g., using apiezoelectric-based device) and RF energy. As will be appreciated, theseare AC energy sources and so would require AC-DC converters. And if theresulting DC voltages from any of these are not sufficiently high, theycould be boosted using a boost converter.

The processing of STM sensor data by an STM algorithm and its componentanalysis algorithms according to a particular class of implementationswill now be described with reference to FIGS. 3-6. As mentioned above,these processing techniques may be used in conjunction with STMs enabledby the present disclosure, but also may be used with any of a widevariety of STM types. As illustrated in FIG. 3, the inputs to the STMalgorithm include time series data, e.g., vectors, for the STMrepresenting steam temperature (T_(steam)), condensate temperature(T_(cond)), (optionally) ambient temperate (T_(amb)), and time stampsfor the corresponding values of the time series data (Time). Vectorextraction (302) generates vector features from these input vectors fora number of different vector types.

According to some implementations, each vector may include featurevalues for the STM over its entire history, and each time the STMalgorithm and its various analysis components runs, this entire historyof the STM may be processed. This approach may be useful in that, todetermine the state of the steam trap at any given point in time, itsbehavior over its lifetime may be relevant and so can be taken intoaccount. That is, the historical behavior of the STM is often useful inunderstanding and/or informing its current behavior.

It should be noted, however, that implementations are contemplated inwhich only a subset of an STM's sensor data for a given time range mightbe processed. For example, if an STM has generated 10 years of sensordata, and there are time constraints on how quickly the state of the STMmust be determined, an STM algorithm might be constrained to usingsensor data from only the most recent year to speed up the processingtime. In addition, repair or calibration of an STM might be introducedinto its data set as a “ground truth” event that can serve to informsubsequent event detections and confidence scores and/or define therange of sensor data to be used.

According to some implementations, the vectors generated as a result ofvector extraction 302 include a dt vector the values of which representthe time difference between consecutive samples in the time series data(e.g., as derived from the Time input vector). As will be discussed,this information is useful in determining the rate of change of any ofthe time series data and may be used, for example, in determining thetiming of steam on and steam off events. As will also be discussed, thedt vector may also be used in detecting steam modulation, and detectingevents representing significant changes in status of the steam trap.

Vector extraction 302 also results in generation of vectors referred toherein as Ts Envelope (max and min) and Tc Envelope (max and min) thatrepresent temperature envelopes for the steam temperature and thecondensate temperature, respectively. Each envelope is represented bytwo vectors including temperature values that are time-aligned to theoriginal temperature values of the corresponding temperature vector,e.g., the values of the Ts Envelope (max and min) vectors aretime-aligned to the values of the Ts vector. One of the envelope'svectors represents the maximum values of the corresponding temperature,while the other represents the minimum values. These representationsadapt slowly to changes in the underlying temperate and are useful fordetecting when these temperatures change in unexpected ways.

Vector extraction 302 may also result in generation of vectors for bothsteam temperature and condensate temperature referred to herein asDelta-T (steam and cond). The values of these Delta-T vectors representthe difference between consecutive temperature samples. This informationmay be useful, for example, in distinguishing between different trapstates and/or failure modes.

Vector extraction 302 might also result in generation of vectorsreferred to herein as Ts Variance Energy and Tc Variance Energy. Theseare generated by feeding Ts and Tc, respectively, through a DC blockingfilter to generate a measure of the energy in each of the signals. Thesevectors are representations of the stability of the correspondingtemperatures (i.e., the steam and condensate temperatures) when they arestable, and can be thought of as a kind of noise floor. Implementationsare contemplated in which either the raw magnitude or the log of the rawmagnitude of the temperature values are used. Again, this informationmay be useful in distinguishing between different trap states and/orfailure modes.

Vector extraction 302 might also result in generation of a vectorreferred to herein as Steam Off Decay Rate which represents the rate atwhich a trap cools off when steam is shut off in the system, e.g., thedecay constant from the edge following a “steam off” event. The vectorcan be derived by low-pass filtering the Ts or Tc temperature data, withabnormal states or conditions being detected by observing the standarddeviation and looking for outliers.

At least some of these vectors are passed through multiple filters (304)to provide different perspectives on the behavior of the vector. Mostvectors receive some degree of low-pass filtering (short- or long-termaveraging). The filtered vectors (from which digital signatures may bederived) are then analyzed using one or more analysis components (306)to generate various outputs, each of which has an associated confidencevalue. Some examples of such analyses components are discussed below.

The outputs from the analysis components may then be processed byanother layer of logic to identify a possible state of the steam trapand/or the steam system (308). The identified state of a trap might bespecific (e.g., normal, failed open, failed closed, etc.).Alternatively, the identified state might just be that something is notnormal and a flag could be set that a specific steam trap should bemanually checked.

According to some implementations, one of the analysis components 306uses steam temperature data to determine whether live steam is flowingat the steam trap. This steam on/off detection analysis is based on theassumption that determination of the state of the steam trap can beaccomplished with a higher degree of reliability if it is known whetheror not the steam is on at the trap. Whether or not steam is flowing tothe trap (also referred to as the “system state”) is determined byprocessing the steam temperature data using multiple determinationmethods as illustrated in the diagram of FIG. 4. Each method (402-1through 402-n) generates a state output estimate vector along with aconfidence vector. The outputs from the different determination methodsare then selected and/or combined (404) to derive an overall systemstate estimate vector and an associated confidence vector. According toa particular implementation, the result of the method with the highestconfidence is used to determine the overall system state. Alternatively,implementations are contemplated in which some combination of theoutputs and confidence levels of the different methods is used.

One method suitable for use in steam on/off detection establishes fixedthresholds for the conditions “steam on” and “steam off” and useshysteresis between the thresholds. The “steam on” threshold might beabout 90 degrees C., while the “steam off” threshold might be about 60degrees C. However, it should be understood that different thresholdsmight be used to better reflect the type of steam trap and theapplication in which it is installed. It should also be understood thatthe hysteresis thresholds do not necessarily need to the same as the onand off thresholds. For example, the hysteresis threshold down to whicha steam on event might be maintained could be 70 degrees C., while thehysteresis threshold up to which an steam off event might be maintainedcould be 80 degrees C.

A confidence value may be calculated such that when the steamtemperature is outside the thresholds, there is a reasonable level ofconfidence (e.g., >50%) that the system state is known. On the otherhand, if the steam temperature is between the thresholds, knowledge ofthe system state is significantly less certain. Parameters that may betuned for this method to suit a particular application or steam traptype include the “steam on” and “steam off” thresholds, and theconfidence level at the thresholds.

Another method suitable for use in steam on/off detection adapts to thesteam trap being monitored by generating “floating” thresholds. This isachieved by tracking the envelope of the steam temperature based on theobserved extremes.

According to this method, steam is considered (1) “on” if the observedsteam temperature exceeds the median value by some programmable value;(2) “off” if the observed temperature is below the median value by someprogrammable value; and (3) unchanged from the previous state if thetemperature falls near the median (i.e., hysteresis).

In a first pass of a particular implementation, multi-segmented logic isapplied to the observed steam temperature as it relates to the existingextremum. This logic operates as follows. If the observed temperatureexceeds the current extremum, update the extremum through a low-passfilter. If the observed temperature is near an extremum (e.g., within aprogrammable value), and the observed temperature is stable (e.g., itsderivative is below a programmable value), then update the extrema usinga second low-pass filter. If the observed temperature is between theextremum and far from an extremum (e.g., outside of some programmablevalue), update the opposite extrema using a third low-pass filter. Ifnone of these conditions are met, then the new extremum value matchestheir previous value.

The confidence vector generated by this method may take into accountvarious parameters such as, for example, the temperature delta betweenthe tracking extremum, the distance between the observed temperature andthe tracked median, and/or the proximity of the tracking extremum toambient temperature, among others. Further stability may also beprovided through the use of an additional low pass filter on the medianvalues.

Another method suitable for use in steam on/off detection involves theaccumulation of ground truth events for particular steam traps (e.g.,repair or recalibration) to inform the thresholds to which observedsamples of time series data are compared. This approach involves the useof Boolean vectors representing each steam trap that include featuresrepresenting, for example, the steam trap type and the specificapplication in which the steam trap is installed. The Boolean vectorsare used to determine how closely the ground truth events for aparticular steam trap should match those of another. For example, atemperature envelope for a given steam trap can be predicted based onanalysis of the ground truth events for steam traps with which the givenstream trap is closely correlated.

Because of the use of hysteresis, the point in time at which a “steamoff” event is reported will likely be delayed relative to the point intime at which the event actually occurred. So in some cases it may beimportant that the determination of such events not rely solely on thecrossing of a threshold. That is, it may be undesirable to evaluate thestate of a steam trap when live steam is not present. And because thereporting of “steam off” event is likely delayed from the point in timeat which the steam actually turned off, the data immediately precedingthe report is considered unreliable for this purpose.

Therefore, according to a specific implementation, the time of theactual “steam off” event is determined. This is done by stepping backthrough the steam temperature data from the point in time at which thestate change is reported to identify the point in time at which thesteam temperature began to drop.

According to some implementations, states and confidence values formultiple STMs may be used to determine a system state, e.g., whethersteam is on or off, with higher confidence. And these may be combinedand/or weighted based on correlations among the traps, e.g., the steamtrap types and the applications in which each is installed. The data foreach STM might have associated metadata that represent variouscharacteristics of the trap with which it is associated such as, forexample, the steam trap type being monitored and the application inwhich the steam trap is installed, e.g., drip, coil, process, heatexchanger, etc. Such metadata may be taken into account in theprocessing of the outputs and confidence scores of the STM algorithm aswell as any of its analysis components.

According to some implementations, one or more of the analysiscomponents 306 of the STM algorithm of FIG. 3 uses the steam on/offstate, steam and condensate temperature data, envelope tracking data,and trap and/or system metadata to determine one or more states of aparticular steam trap. This analysis component identifies steam trapstates based on corresponding changes in a distribution of the steam andcondensate temperature data over time. A particular steam trap state(e.g., failed open) is identified based on the direction and magnitudeof the change.

Assuming live steam is flowing at the steam trap (e.g., the “steam on”system state has been detected as described above) and as depicted inFIG. 5, the center of a “steam on” data cluster is determined byconverting the steam and condensate temperature data and the temperatureenvelope tracking data Ts Envelope (max) and Tc Envelope (max) fromCartesian to polar coordinates (506 and 512).

Changes in the steam and condensate temperature data relative to acentral point defined by the envelope tracking data and having amagnitude greater than a specified level (e.g., 1 to 3 standarddeviations) (514 and 516), and a direction bounded by a specified rangeof angles (518) are flagged. Flagged events are reported with associatedconfidence scores (520).

The specified range of angles that define the direction of movement ofthe trap temperature data and/or the specified magnitude of the changemay vary considerably depending on the trap type and the trapapplication, with different ranges of angles potentially representingdifferent trap states or failure modes.

In many cases, steam traps are installed in applications in which thetrap experiences considerable variation in the behavior of the livesteam at the steam side of the trap under normal operating conditions.These normal operating conditions can look very different from thenormal operating conditions for traps that experience more consistentlive steam behavior. For example, for a trap that cycles on and offrapidly, the steam and/or condensate temperatures typically do not reachthe same extremes as for a trap that is on or off for longer periods oftime. This is to be expected, as the thermal mass of the system takes along time to get to full temperature, effectively acting as a low-passfilter.

In another example, a trap might be installed on the opposite side of aload from the steam distribution system at a point at which condensateis expected to form during normal operation, e.g., a system with a heatexchanger having a trap below the exchanger. In such an application, thesteam side of the trap would be expected to have a considerable amountof condensate under normal conditions, bringing the expected steamtemperature down significantly as compared to a trap connected directlyto the steam distribution system. For example, in such applications, thesteam temperature for the “steam on” condition is lower than a trap onthe steam supply. In addition, the temperature of the load might beregulated using a valve on top of the heat exchanger that cycles on andoff to control the amount of steam going to the exchanger. This causesthe trap temperatures to rise and fall according to a regular patternwith consistent frequency content.

These variations in the conditions experienced by steam traps in suchapplications are known collectively by the term “modulated steam.” Aswill be appreciated, it may be important to determine whether aparticular trap is experiencing modulated steam as part of determiningthe state of the trap.

Therefore, according to some implementations, one or more of theanalysis components 306 of the STM algorithm of FIG. 3 uses the steamtemperature data for a steam trap to detect whether that trap isdeployed in a modulated steam application. Such analysis components areconfigured to detect regular or even semi-regular cycling in the steamtemperature.

According to one class of implementations, the steam temperature dataare converted to the frequency domain (e.g., using a Fast FourierTransform or FFT) to determine the frequency and/or magnitude of anycycles in the temperature data. Such an approach may be desirable incases where the magnitudes of the frequency components are important. Onthe other hand, this approach might not be particularly well suited ifthere is not a high degree of confidence that the sampling rate for thetemperature data will be consistent.

According to another class of implementations, an autocorrelationalgorithm may be used to determine the overall period of any repeatingcycle in the steam temperature data. The use of an autocorrelationalgorithm is also particularly well suited to implementations which seekonly to determine whether or not cycling is occurring as opposed todetermining the constituent frequency components of the cycling.

A particular implementation of a modulation detection analysis using anautocorrelation algorithm will now be described with reference to FIG.6. However, it should be noted that the present disclosure bothcontemplates and enables implementations in which frequency conversion(e.g., using an FFT) is employed, either as an alternative or incombination with an autocorrelation function.

Regardless of the technique used to detect temperature cycling, thesteam temperature data (Ts) are preconditioned prior to analysis; inthis specific implementation, using a band pass filter 602. Suchpreconditioning may be useful, for example, in cases in which the steamtemperature data have a large DC offset. Use of a DC blocking filterwith a relatively steep roll off prevents overflow of theautocorrelation algorithm (or masking of the low frequency components ofthe FFT output). Such filtering may also be useful for implementationsin which high frequency changes from sample to sample are considered tobe noise.

An autocorrelation algorithm (606) is run on the steam temperature datausing a regular number of samples. The autocorrelation algorithm may,but is not required to, account for missing samples or variations insample timing.

A variety of post-processing techniques may be applied to the rawautocorrelation output prior to its interpretation. For example, centralpeak removal involves “de-mirroring” of the raw autocorrelation outputfollowed by removal of the central peak. For a cyclic signal, the outputof an autocorrelation algorithm is mirrored about the central (zerodelay) point. This means that half of the output vector can be discardedwithout incurring any loss of information. In one approach, the firsthalf of the vector is discarded as this results in a vector arranged inorder of increasing delay (starting at zero).

As is well known, there will always be a peak at the zero delay point,and it will always be the highest level of the autocorrelation output.This is because at zero delay, there is a 100% correlation of thesignal, and no other alignment can result in better than 100%correlation. However, because we are trying to identify a cyclic delay,this peak (which represents zero delay) is irrelevant, so it isadvantageous to remove it. The peak can be removed by analyzing thefirst derivative of the autocorrelation output to isolate the centralpeak from any adjacent peaks. Since the central peak is guaranteed to bethe highest peak, the first derivative to either side of the peak isguaranteed to be negative. Moving along the autocorrelation output, thefirst derivative will turn positive at the foot of the next peak,however slightly. Thus, simple logical analysis allows us to determinethe limits of the central peak. The peak is then removed by setting allvalues in the central section of the data to be equal to the first valueoutside that section. If the peak occupies the entire block, thecorrelation for the entire block is set to zero.

The absolute magnitude of the various peaks in the autocorrelationoutput may not be particularly important as it is a function of themagnitude of the input signal. And since no amount of delay can producea higher correlation than the 100% correlation found at the centralpeak, it may be advantageous to represent all other outputs as relativeto the central point. This gives a correlation factor that isirrespective of signal level. Thus, the autocorrelation output may benormalized to the central (zero delay) point. This gives a meaningfulrepresentation of how well the signal repeats itself at various delayamount.

In addition to the application of the autocorrelation algorithm (706), arepresentation of the energy of each block of temperature data isdetermined (716). If the autocorrelation output has been normalized, itmay no longer include sufficient information for determining whether itrepresents actual cycling of the trap (e.g., several degrees oftemperature swing) or only a slight undulation in the temperaturereadings (e.g., spanning only one or two degrees). Determining theenergy level of the original input block can be used to resolve thisambiguity.

The autocorrelation vector and the energy measurement vector are fedthrough thresholding and qualification logic 718 to qualify them asrepresentative of modulated steam. All threshold outputs are combinedusing a logical AND statement. In some cases, a simple thresholding ofthe inputs may be done.

It will be understood by those skilled in the art that changes in theform and details of the implementations described herein may be madewithout departing from the scope of this disclosure. In addition,although various advantages, aspects, and objects have been describedwith reference to various implementations, the scope of this disclosureshould not be limited by reference to such advantages, aspects, andobjects. Rather, the scope of this disclosure should be determined withreference to the appended claims.

What is claimed is:
 1. A sensing device, comprising: one or moretemperature sensors configured to generate sensor signals representingone or more temperatures; one or more transmitters configured towirelessly transmit sensor data representing the sensor signals to aremote device; control circuitry configured to control operation of theone or more transmitters, and to convert the sensor signals to thesensor data, the sensor data comprising time series data representingthe one or more temperatures; a thermo-electric generator configured toharvest heat energy from an environment in which the sensing device isdeployed; and an energy storage device configured to store energyderived from the heat energy.
 2. The sensing device of claim 1, furthercomprising: a second energy harvesting device configured to harvest asecond type of energy from the environment, the second type of energybeing different from heat energy; power management circuitry configuredto receive first power from the thermo-electric generator and secondpower from the second energy harvesting device, and control storing ofthe stored energy by the energy storage device, the power managementcircuitry being further configured to provide third power generated fromthe stored energy to the one or more transmitters and the controlcircuitry.
 3. The sensing device of claim 2, wherein the powermanagement circuitry is further configured to operate in a first mode inwhich the stored energy is derived only from the first power, in asecond mode in which the stored energy is derived only from the secondpower, and in a third mode in which the stored energy is derived from acombination of the first power and the second power.
 4. The sensingdevice of claim 3, further comprising selection circuitry configured toselectively enable the power management circuitry to operate in thefirst, second, or third modes.
 5. The sensing device of claim 2, whereinthe power management circuitry includes power-point-tracking circuitryconfigured to track either or both of the first power and the secondpower.
 6. The sensing device of claim 2, wherein the second type ofenergy is one of light energy, mechanical energy, vibrational energy,acoustic energy, ultrasonic energy, radio frequency energy,electromagnetic energy, or magnetic energy.
 7. The sensing device ofclaim 2, wherein the power management circuitry includes at least one ofa DC-DC converter, an AC-DC converter, a buck converter, a boostconverter, a buck-boost converter, or a single inductor multiple output(SIMO) converter.
 8. The sensing device of claim 2, wherein the secondenergy harvesting device comprises one or more photovoltaic cells. 9.The sensing device of claim 1, wherein the energy storage device iseither a capacitor or a battery.
 10. The sensing device of claim 1,wherein the sensing device is configured to monitor a steam trap,wherein the one or more temperature sensors include a first temperaturesensor and a second temperature sensor, and wherein the sensor datarepresent steam temperature and condensate temperature.
 11. The sensingdevice of claim 1, wherein the sensor data include first time seriesdata representing the steam temperature and second time series datarepresenting the condensate temperature.
 12. The sensing device of claim1, wherein the sensor data also represent either or both of an ambienttemperature or a humidity of the environment in which the sensing deviceis deployed.
 13. The sensing device of claim 1, wherein the sensor dataalso represent one or more of light energy, mechanical energy,vibrational energy, acoustic energy, ultrasonic energy, radio frequencyenergy, electromagnetic energy, or magnetic energy.
 14. The sensingdevice of claim 1, wherein the control circuitry is configured to wakeup from a low-power mode to take readings of the sensor signals, controloperation of the one or more transmitters, and convert the sensorsignals to the sensor data.
 15. The sensing device of claim 14, furthercomprising a wakeup timer configured to generate a wakeup message, or awakeup receiver configured to receive a wakeup message from the remotedevice.
 16. The sensing device of claim 1, wherein the control circuitryis configured to packetize the sensor data for transmission to theremote device, each packet of the sensor data identifying the sensingdevice and having a timestamp for the associated sensor data.
 17. Thesensing device of claim 16, wherein the one or more temperature sensorscomprise a plurality of temperature sensors, and wherein each packetincludes information correlating portions of the associated sensor datawith corresponding ones of the plurality of temperature sensors.
 18. Asteam trap monitor, comprising: a plurality of temperature sensorsconfigured to generate sensor signals representing a steam temperatureand a condensate temperature associated with a steam trap; one or moretransmitters configured to wirelessly transmit sensor data representingthe sensor signals to a remote device; a wakeup receiver configured toreceive a wakeup message from the remote device; control circuitryconfigured to control operation of the one or more transmitters, and toconvert the sensor signals to the sensor data, the sensor datacomprising first time series data representing the steam temperature andsecond time series data representing the condensate temperature, whereinthe control circuitry is further configured to wake up from a low-powermode in response to the wakeup message to take readings of the sensorsignals, control operation of the one or more transmitters, and convertthe sensor signals to the sensor data; a thermo-electric generatorconfigured to harvest heat energy from an environment in which the steamtrap monitor is deployed; and an energy storage device configured tostore energy derived from the heat energy.
 19. The steam trap monitor ofclaim 18, wherein the control circuitry is configured to packetize thesensor data for transmission to the remote device, each packet of thesensor data identifying the steam trap monitor, including a timestampfor the associated sensor data, and including information correlatingportions of the associated sensor data with corresponding ones of theplurality of temperature sensors.
 20. The steam trap monitor of claim18, further comprising: a photovoltaic cell; and power managementcircuitry configured to receive first power from the thermo-electricgenerator and second power from the photovoltaic cell, and controlstoring of the stored energy by the energy storage device, the powermanagement circuitry being further configured to operate in a first modein which the stored energy is derived only from the first power, in asecond mode in which the stored energy is derived only from the secondpower, and in a third mode in which the stored energy is derived from acombination of the first power and the second power.